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Lender Behavior During Credit Cycles Giovanni Dell'Ariccia Deniz Igan Luc Laeven

Lender Behavior During Credit Cycles Giovanni Dell'Ariccia Deniz Igan Luc Laeven. Comments: Alejandro Micco. Summary. The main aim is to understand the determinant of mortgage denial rates in the context of the recent credit boom: Financial innovation Strategic interaction among lenders

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Lender Behavior During Credit Cycles Giovanni Dell'Ariccia Deniz Igan Luc Laeven

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  1. Lender Behavior During Credit CyclesGiovanni Dell'AricciaDeniz IganLuc Laeven Comments:Alejandro Micco

  2. Summary • The main aim is to understand the determinant of mortgage denial rates in the context of the recent credit boom: • Financial innovation • Strategic interaction among lenders • credit growth by competitors led lenders to grant loans more liberally, resulting in a race to the bottom in credit quality. • less capitalized lenders appear to have behaved more aggressively in their lending decisions • Policy conclusion • Capital matters. • role for regulatory responses to cyclical competitive pressures.

  3. Previous studies • Financial innovation (securitization ), deregulation, and the widespread failure of the supervisory and regulatory frameworks have often been blamed as the culprits (and GSEs). • Keys et al. (2007) and Ashcraft and Schuermann (2007) argue that securitization played an important role in the expansion of the mortgage market. • Loutskina and Strahan (2007) shows how securitization affects the supply of loans and mortgage delinquencies. • evidence on how lending standards are related to credit cycles : • Asea and Blomberg (1998) find that loan collateralization increases during contractions • Lown and Morgan (2003) show that lending standards are associated with innovations in credit. • Jimenez, Salas, and Saurina (2006) find that during booms riskier borrowers obtain credit and collateral requirements decrease

  4. Data • Home Mortgage Disclosure ActHDMA: dataset on mortgage applications • Denials • House: location of the property/ it is owner-occupied or not • Borrower: income, gender, race • Loan: amount, purpose, GSE-insured or not • But no interest rates, FICO scores, and loan-to-value ratios • They drop federally insured loans. • Call Report files:data on banks’ balance sheets and income statements • Macro Data at the MAS level. • Period: 2000 to 2007 • Panel: Time / Metropolitan Statistical Area / Lender

  5. Methodology Change in the denial rate: demand or supply shock? Period FE Bank-Market FE Main variable: # of application

  6. Some questions: • Dependent Variable: Demand or Supply Shock? • Subprime / Subprime loans. • Economic effect of some explanatory variables • Fixed Effect implies first derivative. Then • House Price appreciation (is the second derivate) • Income Growth (is the second derivate) • House Price (Huge) • Loan to Income. Important variable but small economic effect.

  7. Dep.Var: SD 7.9 Large effect: SD: 5.3 Significant but 2nd Derivative. Information? SD: 0.29 (Important variable) Information? Strahan

  8. Dep.Var: SD 7.9 “denial rates tended todecline with the number of loan applications. More precisely, denial rates tended to drop in regions where applications increased, as measured by the log number of applications to other lenders in MSA variable, but increased with the number of applications at each individual lender. The overall effect of an increase of loan applications on denial rates is strongly negative as the first effect outweighs the second effect, and the difference is statistically significant.” Assuming a log change: SD : 1.32 Dif : 0.625-0.574=0.051 => 0.067 (?)

  9. Adverse Selection • An increase in market application implies a reduction on denial rates. • .. • A movement of clients from others lenders to the bank implies a reduction on denial rates. • - -0.574 + 0.625 • Larger Share: less Choosy (reduce the adverse selection problem). • or more market information? Strahan.

  10. Bank Size • Large banks cause the crises. • Large bank less denial rate. • Nationwide denial rate (coef = 0.74) • lender’s nationwide denial rate appears to explain a large portion of the denial rate variability. • Endogenous? 1 to 1.

  11. Capital adequacy • More capitalized bank tended to have higher denial rates.

  12. Dep.Var: SD 7.9

  13. Capital adequacy • More capitalized bank tended to have higher denial rates. • The effect is small: • Coef = 19.22 * SD = 0.03 • Effect: 0.57 • SD Denial = 7.9

  14. New Entrant • Reduce denial. • New entrants have less private information, therefore they have more incentive to sell loans (to Government Sponsored Enterprises Fannie Mae). • Concentrate lenders has lower denial rates, but only in the primary market. • See Loutskina & Strahan 2009

  15. Conclusion • Denial rates: • Financial innovation • Securitization • Strategic interaction among lenders • credit growth by competitors led lenders to grant loans more liberally, resulting in a race to the bottom in credit quality. • less capitalized lenders appear to have behaved more aggressively in their lending decisions • Policy conclusion • Capital matters. • role for regulatory responses to cyclical competitive pressures.

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